title: Pharmacodynamic modeling of the effect of changes in the environment on cellular lifespan and cellular response.
abstract: Lifespan-based pharmacodynamic (PD) models of cellular response assume that the lifespan of cells is predetermined at the time of cellular production, despite recognized changes in the cellular environment following production that may alter the survival of the cells. This work extends previously proposed cellular lifespan PD models to incorporate environmental effects on the cell lifespan by considering two basic classes of models from survival analysis: accelerated life and relative risk models. Cellular responses using both model classes were simulated using a steady-state cellular production rate with changes in the environmental effects resulting from three different basic profiles. The environmental effect models were also fitted to the red blood cell (RBC) and hemoglobin concentration data from six sheep following hematopoietic ablation by busulfan administration. The simulations indicated that the basic shapes of the cellular responses were different between the accelerated life and relative risk models. Due to the more direct physical interpretation, relatively simple steady-state relationship between the cellular response and environmental effects, and the ability to reduce the model to a "point" baseline lifespan distribution, the accelerated life model appears to be a more realistic and flexible model. The analysis of the sheep RBC and hemoglobin data indicated that the environmental effect began to decrease the survival of cells 1-2 weeks following initiation of ablation and that the average "severity" of the environmental effect increased 3.49 (29.5%) (mean (C.V.)) fold under the accelerated life model. Alternative models without an environmental effect did not describe the observed data as well. The proposed environmental effect cellular lifespan PD models allow for the incorporation of arbitrary changes in the conditions of the cellular environment and modeling of environmentally dependent cellular survival. These PD models have potential applications in hematological management of end-stage renal disease, transfusion medicine, and patients undergoing chemotherapy, among other diseases and therapies.
authors.affiliation: College of Pharmacy, University of Iowa, 115 S. Grand Ave., Iowa City, IA, 52242, USA.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Freise
authors.forename: Robert L
authors.initials: RL
authors.lastname: Schmidt
authors.forename: John A
authors.initials: JA
authors.lastname: Widness
authors.forename: Peter
authors.initials: P
authors.lastname: Veng-Pedersen
mesh_terms: D000465:Algorithms; D000818:Animals; D002066:Busulfan / Q000494:pharmacology; D002470:Cell Survival / Q000187:drug effects; D003198:Computer Simulation; D004906:Erythrocyte Count; D004912:Erythrocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D005057:Eukaryotic Cells / Q000166:cytology* / Q000187:drug effects*; D006454:Hemoglobins / Q000032:analysis; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D019653:Myeloablative Agonists / Q000494:pharmacology; D010600:Pharmacology / Q000706:statistics & numerical data*; D012756:Sheep; D013997:Time Factors
chemical_list: D006454:Hemoglobins; D019653:Myeloablative Agonists; D002066:Busulfan